The collection of as-built data for construction progress monitoring remains challenging. This paper develops two case studies on image-based modeling, in which point cloud models are created based on the photo collection of the construction site. The first case considers 399 unordered construction images previously taken for purposes other than progress monitoring, whereas the second case considers 118 photos that have been taken based on the results of the first case study. The results of the first case study are employed to improve the quality of the point cloud model in the second case, using the site photo collection captured by the first author for the purpose of establishing an enhanced point cloud model. The results of the two case studies are compared. Furthermore, the results are compared with those of other researchers and found that they are in a good agreement with other reported results. Finally, some suggestions are proposed to improve the image-based model for construction progress monitoring, particularly for industrial projects that involve a large construction site and various work packages.

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Introduction: Visualization of as-built data may change the future of construction project management to a more efficient area of knowledge if appropriate and easy photography and reconstruction tools would be developed and used by practitioners. Most of the current reconstructed 3D point cloud models use unordered photograph collections to generate 4D as-built models. Some of these photographs are not used in the final model mostly because of either a possible overlap with other photos or some faults in photography procedure. Computation time increases exponentially as the number of photos in a photo collection increases. Therefore, the unstructured processes may reduce the performance of a point-cloud model representation. This work shows how the available application of unordered photograph collections are regularly inefficient by measuring the performance of some important criteria, such as the registration success score and the computation time.Case description: The case study is the construction of a gas compressor station. Such industrial projects involve several building and work areas (e.g., substation, control building, and piping area). The construction site covers approximately 20 hectares. The case study was conducted in two stages. In the first stage, preexisting images have been used for image based modelling (IBM). In the second stage, images captured based on a step-by-step photography procedure (SPP) have been used for IBM. Discussion and evaluation:IBM performance in the first stage of the case study has been compared with the performance of the second stage by comparing the registration success scores. The IBM in the first stage of the case study results in sparse models, which hardly show the geometry of construction scenes. By contrast, capturing images based on the SPP in the second stage of the case study significantly changed the performance of IBM and increased the registration success score. Conclusion:This study provides an easy applicable on-site photography procedure. By adopting the proposed approach and by training the photographers, the model would be more desirable for application in more construction projects. The application of the SPP in the case study shows a significant improvement in the final reconstructed 3D point cloud model and as-built data visualization criteria.

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